import time
from options.train_options import TrainOptions
from data import CreateDataLoader
from models import create_model
from util.visualizer import Visualizer

if __name__ == '__main__':
    opt = TrainOptions().parse()

    # Loss parameters
    opt.lambda_A = 1.0
    opt.lambda_B = 1.0
    opt.idt_w = 1.0
    opt.kl_lambda = 0.001
    opt.feat_weight = 0.001

    # Display port for visdom server
    opt.display_port = 8097

    # opt.display_id = 0 for not using the visdom server
    opt.display_id = 1

    data_loader = CreateDataLoader(opt)
    dataset = data_loader.load_data()
    dataset_size = len(data_loader)
    print(opt.name)

    model = create_model(opt)

    visualizer = Visualizer(opt)
    total_steps = 0
    count_alter = 0
    for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
        epoch_start_time = time.time()
        iter_data_time = time.time()
        epoch_iter = 0
        model.opt.epoch = epoch
        for i, data in enumerate(dataset):
            count_alter += 1
            model.step = i
            iter_start_time = time.time()
            if total_steps % opt.print_freq == 0:
                t_data = iter_start_time - iter_data_time
            visualizer.reset()
            total_steps += opt.batchSize
            epoch_iter += opt.batchSize
            model.set_input(data)

            model.optimize_parameters()

            if total_steps % opt.display_freq == 0:
                save_result = total_steps % opt.update_html_freq == 0
                visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)

            if total_steps % opt.print_freq == 0:
                errors = model.get_current_errors()
                t = (time.time() - iter_start_time) / opt.batchSize
                visualizer.print_current_errors(epoch, epoch_iter, errors, t, t_data)
                if opt.display_id > 0:
                    visualizer.plot_current_errors(epoch, float(epoch_iter) / dataset_size, opt, errors)

            if total_steps % opt.save_latest_freq == 0:
                print('saving the latest model (epoch %d, total_steps %d)' %
                      (epoch, total_steps))
                model.save('latest')

            iter_data_time = time.time()
        if epoch % opt.save_epoch_freq == 0:
            print('saving the model at the end of epoch %d, iters %d' %
                  (epoch, total_steps))
            model.save('latest')
            model.save(epoch)

        print(opt.name)
        print('End of epoch %d / %d \t Time Taken: %d sec' %
              (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
        model.update_learning_rate()

